OpenRaiser/Intern-Atlas huggingface dataset:
Finding by Counting: A Probabilistic Packet Count Model for Indoor Localization in BLE Environments
Venue: arXiv cs.NI 2017
Year: 2017

We propose a probabilistic packet reception model for Bluetooth Low Energy (BLE) packets in indoor spaces and we validate the model by using it for indoor localization. We expect indoor localization to play an important role in indoor public spaces in the future. We model the probability of reception of a packet as a generalized quadratic function of distance, beacon power and advertising frequency. Then, we use a Bayesian formulation to determine the coefficients of the packet loss model using empirical observations from our testbed. We develop a new sequential Monte-Carlo algorithm that uses our packet count model. The algorithm is general enough to accommodate different spatial configurations. We have good indoor localization experiments: our approach has an average error of ~1.2m, 53% lower than the baseline range-free Monte-Carlo localization algorithm.
TorPolice: Towards Enforcing Service-Defined Access Policies in Anonymous Systems
Venue: arXiv cs.CR 2017
Year: 2017

Tor is the most widely used anonymity network, currently serving millions of users each day. However, there is no access control in place for all these users, leaving the network vulnerable to botnet abuse and attacks. For example, criminals frequently use exit relays as stepping stones for attacks, causing service providers to serve CAPTCHAs to exit relay IP addresses or blacklisting them altogether, which leads to severe usability issues for legitimate Tor users. To address this problem, we propose TorPolice, the first privacy-preserving access control framework for Tor. TorPolice enables abuse-plagued service providers such as Yelp to enforce access rules to police and throttle malicious requests coming from Tor while still providing service to legitimate Tor users. Further, TorPolice equips Tor with global access control for relays, enhancing Tor's resilience to botnet abuse. We show that TorPolice preserves the privacy of Tor users, implement a prototype of TorPolice, and perform extensive evaluations to validate our design goals.
Risk-Sensitive Optimal Control of Queues
Venue: arXiv cs.SY 2017
Year: 2017

We consider the problem of designing risk-sensitive optimal control policies for scheduling packet transmissions in a stochastic wireless network. A single client is connected to an access point (AP) through a wireless channel. Packet transmission incurs a cost $C$, while packet delivery yields a reward of $R$ units. The client maintains a finite buffer of size $B$, and a penalty of $L$ units is imposed upon packet loss which occurs due to finite queueing buffer. We show that the risk-sensitive optimal control policy for such a simple set-up is of threshold type, i.e., it is optimal to carry out packet transmissions only when $Q(t)$, i.e., the queue length at time $t$ exceeds a certain threshold $τ$. It is also shown that the value of threshold $τ$ increases upon increasing the cost per unit packet transmission $C$. Furthermore, it is also shown that a threshold policy with threshold equal to $τ$ is optimal for a set of problems in which cost $C$ lies within an interval $[C_l,C_u]$. Equations that need to be solved in order to obtain $C_l,C_u$ are also provided.
An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we report an optimized union-find (UF) algorithm that can label the connected components on a 2D image efficiently by employing the GPU architecture. The proposed method contains three phases: UF-based local merge, boundary analysis, and link. The coarse labeling in local merge reduces the number atomic operations, while the boundary analysis only manages the pixels on the boundary of each block. Evaluation results showed that the proposed algorithm speed up the average running time by more than 1.3X.
Active Animations of Reduced Deformable Models with Environment Interactions
Venue: arXiv cs.GR 2017
Year: 2017

We present an efficient spacetime optimization method to automatically generate animations for a general volumetric, elastically deformable body. Our approach can model the interactions between the body and the environment and automatically generate active animations. We model the frictional contact forces using contact invariant optimization and the fluid drag forces using a simplified model. To handle complex objects, we use a reduced deformable model and present a novel hybrid optimizer to search for the local minima efficiently. This allows us to use long-horizon motion planning to automatically generate animations such as walking, jumping, swimming, and rolling. We evaluate the approach on different shapes and animations, including deformable body navigation and combining with an open-loop controller for realtime forward simulation.
A Double Auction Mechanism for Mobile Crowd Sensing with Data Reuse
Venue: arXiv cs.GT 2017
Year: 2017

Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a flexible and scalable sensing coverage with a low deployment cost, by employing mobile users/devices to perform sensing tasks. In this work, we propose a novel MCS framework with data reuse, where multiple tasks with common data requirement can share (reuse) the common data with each other through an MCS platform. We study the optimal assignment of mobile users and tasks (with data reuse) systematically, under both information symmetry and asymmetry, depending on whether the user cost and the task valuation are public information. In the former case, we formulate the assignment problem as a generalized Knapsack problem and solve the problem by using classic algorithms. In the latter case, we propose a truthful and optimal double auction mechanism, built upon the above Knapsack assignment problem, to elicit the private information of both users and tasks and meanwhile achieve the same optimal assignment as under information symmetry. Simulation results show by allowing data reuse among tasks, the social welfare can be increased up to 100~380%, comparing with those without data reuse. We further show that the proposed double auction is not budget balance for the auctioneer, mainly due to the data reuse among tasks. To this end, we further introduce a reserve price into the double auction (for each data item) to achieve a desired tradeoff between the budget balance and the social efficiency.
A Type System for Privacy Properties (Technical Report)
Venue: arXiv cs.CR 2017
Year: 2017

Mature push button tools have emerged for checking trace properties (e.g. secrecy or authentication) of security protocols. The case of indistinguishability-based privacy properties (e.g. ballot privacy or anonymity) is more complex and constitutes an active research topic with several recent propositions of techniques and tools. We explore a novel approach based on type systems and provide a (sound) type system for proving equivalence of protocols, for a bounded or an unbounded number of sessions. The resulting prototype implementation has been tested on various protocols of the literature. It provides a significant speed-up (by orders of magnitude) compared to tools for a bounded number of sessions and complements in terms of expressiveness other state-of-the-art tools, such as ProVerif and Tamarin: e.g., we show that our analysis technique is the first one to handle a faithful encoding of the Helios e-voting protocol in the context of an untrusted ballot box.
Relativized Separation of Reversible and Irreversible Space-Time Complexity Classes
Venue: arXiv cs.ET 2017
Year: 2017

Reversible computing can reduce the energy dissipation of computation, which can improve cost-efficiency in some contexts. But the practical applicability of this method depends sensitively on the space and time overhead required by reversible algorithms. Time and space complexity classes for reversible machines match conventional ones, but we conjecture that the joint space-time complexity classes are different, and that a particular reduction by Bennett minimizes the space-time product complexity of general reversible computations. We provide an oracle-relativized proof of the separation, and of a lower bound on space for linear-time reversible simulations. A non-oracle proof applies when a read-only input is omitted from the space accounting. Both constructions model one-way function iteration, conjectured to be a problem for which Bennett's algorithm is optimal.
On denoising autoencoders trained to minimise binary cross-entropy
Venue: arXiv cs.CV 2017
Year: 2017

Denoising autoencoders (DAEs) are powerful deep learning models used for feature extraction, data generation and network pre-training. DAEs consist of an encoder and decoder which may be trained simultaneously to minimise a loss (function) between an input and the reconstruction of a corrupted version of the input. There are two common loss functions used for training autoencoders, these include the mean-squared error (MSE) and the binary cross-entropy (BCE). When training autoencoders on image data a natural choice of loss function is BCE, since pixel values may be normalised to take values in [0,1] and the decoder model may be designed to generate samples that take values in (0,1). We show theoretically that DAEs trained to minimise BCE may be used to take gradient steps in the data space towards regions of high probability under the data-generating distribution. Previously this had only been shown for DAEs trained using MSE. As a consequence of the theory, iterative application of a trained DAE moves a data sample from regions of low probability to regions of higher probability under the data-generating distribution. Firstly, we validate the theory by showing that novel data samples, consistent with the training data, may be synthesised when the initial data samples are random noise. Secondly, we motivate the theory by showing that initial data samples synthesised via other methods may be improved via iterative application of a trained DAE to those initial samples.
An inexact subsampled proximal Newton-type method for large-scale machine learning
Venue: arXiv cs.LG 2017
Year: 2017

We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an $ε$-suboptimal point in $\tilde{\mathcal{O}}(d(n + \sqrt{κd})\log(\frac{1}ε))$ FLOPS, where $n$ is number of samples, $d$ is feature dimension, and $κ$ is the condition number. As long as $n > d$, the proposed method is more efficient than state-of-the-art accelerated stochastic first-order methods for non-smooth regularizers which requires $\tilde{\mathcal{O}}(d(n + \sqrt{κn})\log(\frac{1}ε))$ FLOPS. The key idea is to form the subsampled Newton subproblem in a way that preserves the finite sum structure of the objective, thereby allowing us to leverage recent developments in stochastic first-order methods to solve the subproblem. Experimental results verify that the proposed algorithm outperforms previous algorithms for $\ell_1$-regularized logistic regression on real datasets.
ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps
Venue: arXiv cs.SE 2017
Year: 2017

We consider the problem of generating relevant execution traces to test rich interactive applications. Rich interactive applications, such as apps on mobile platforms, are complex stateful and often distributed systems where sufficiently exercising the app with user-interaction (UI) event sequences to expose defects is both hard and time-consuming. In particular, there is a fundamental tension between brute-force random UI exercising tools, which are fully-automated but offer low relevance, and UI test scripts, which are manual but offer high relevance. In this paper, we consider a middle way---enabling a seamless fusion of scripted and randomized UI testing. This fusion is prototyped in a testing tool called ChimpCheck for programming, generating, and executing property-based randomized test cases for Android apps. Our approach realizes this fusion by offering a high-level, embedded domain-specific language for defining custom generators of simulated user-interaction event sequences. What follows is a combinator library built on industrial strength frameworks for property-based testing (ScalaCheck) and Android testing (Android JUnit and Espresso) to implement property-based randomized testing for Android development. Driven by real, reported issues in open source Android apps, we show, through case studies, how ChimpCheck enables expressing effective testing patterns in a compact manner.
Why feature dependencies challenge the requirements engineering of automotive systems: An empirical study
Venue: arXiv cs.SE 2017
Year: 2017

Functional dependencies and feature interactions in automotive software systems are a major source of erroneous and deficient behavior. To overcome these problems, many approaches exist that focus on modeling these functional dependencies in early stages of system design. However, there are only few empirical studies that report on the extent of such dependencies in industrial software systems and how they are considered in an industrial development context. In this paper, we analyze the functional architecture of a real automotive software system with the aim to assess the extent, awareness and importance of interactions between features of a future vehicle. Our results show that within the functional architecture at least 85% of the analyzed vehicle features depend on each other. They furthermore show that the developers are not aware of a large number of these dependencies when they are modeled solely on an architectural level. Therefore, the developers mention the need for a more precise specification of feature interactions, e.g., for the execution of comprehensive impact analyses. These results challenge the current development methods and emphasize the need for an extensive modeling of features and their dependencies in requirements engineering.
Machine Learning Approach for Detection of nonTor Traffic
Venue: arXiv cs.CR 2017
Year: 2017

Intrusion detection has attracted a considerable interest from researchers and industries. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymising the identity of internet users connecting through a series of tunnels and nodes. This work focuses on the classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users. A study to compare the reliability and efficiency of Artificial Neural Network and Support vector machine in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset is presented in this paper. The results are analysed based on the overall accuracy, detection rate and false positive rate of the two algorithms. Experimental results show that both algorithms could detect nonTor traffic in the dataset. A hybrid Artificial neural network proved a better classifier than SVM in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset.
Multi-view Low-rank Sparse Subspace Clustering
Venue: arXiv cs.CV 2017
Year: 2017

Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views. Relying on the importance of both low-rank and sparsity constraints in the construction of the affinity matrix, we introduce the objective that balances between the agreement across different views, while at the same time encourages sparsity and low-rankness of the solution. Related low-rank and sparsity constrained optimization problem is for each view solved using the alternating direction method of multipliers. Furthermore, we extend our approach to cluster data drawn from nonlinear subspaces by solving the corresponding problem in a reproducing kernel Hilbert space. The proposed algorithm outperforms state-of-the-art multi-view subspace clustering algorithms on one synthetic and four real-world datasets.
Supervisor Localization for Large-Scale Discrete-Event Systems under Partial Observation
Venue: arXiv cs.SY 2017
Year: 2017

Recently we developed partial-observation supervisor localization, a top-down approach to distributed control of discrete-event systems (DES) under partial observation. Its essence is the decomposition of the partial-observation monolithic supervisor into partial-observation local controllers for individual controllable events. In this paper we extend the partial-observation supervisor localization to large-scale DES, for which the monolithic supervisor may be incomputable. Specifically, we first employ an efficient heterarchical supervisor synthesis procedure to compute a heterarchical array of partial-observation decentralized supervisors and partial-observation coordinators. Then we localize each of these supervisors/coordinators into partial-observation local controllers. This procedure suggests a systematic approach to the distributed control of large-scale DES under partial observation. The results are illustrated by a system of automatic guided vehicles (AGV) serving a manufacturing workcell.
Learning in the Repeated Secretary Problem
Venue: arXiv cs.GT 2017
Year: 2017

In the classical secretary problem, one attempts to find the maximum of an unknown and unlearnable distribution through sequential search. In many real-world searches, however, distributions are not entirely unknown and can be learned through experience. To investigate learning in such a repeated secretary problem we conduct a large-scale behavioral experiment in which people search repeatedly from fixed distributions. In contrast to prior investigations that find no evidence for learning in the classical scenario, in the repeated setting we observe substantial learning resulting in near-optimal stopping behavior. We conduct a Bayesian comparison of multiple behavioral models which shows that participants' behavior is best described by a class of threshold-based models that contains the theoretically optimal strategy. Fitting such a threshold-based model to data reveals players' estimated thresholds to be surprisingly close to the optimal thresholds after only a small number of games.
Node Authentication Using BLS Signature in Distributed PKI Based MANETS
Venue: arXiv cs.CR 2017
Year: 2017

Authenticating a node in mobile ad-hoc networks is a challenging task due to their dynamic and resource constraint infrastructure. For this purpose, MANETS adopt two kinds of approaches Public key cryptography and identity-based cryptography. In Public Key Infrastructure (PKI), Certificate Authority (CA) is responsible for key management. In order to adapt it to MANET, the job of the CA must be distributed. The master secret key is shared among the nodes of the MANET, to self-organize the network without a central authority.The key is shared based on Shamir secret sharing scheme with bivariate polynomial to make the MANET fully self-managed by nodes.In this paper, we considered PKI based scenario and proposed a new scheme to authenticate a node using BLS signature which is light weight compared to the existing schemes thus making it suitable for MANET.
Towards combinatorial modeling of wireless technology generations
Venue: arXiv cs.NI 2017
Year: 2017

The paper addresses the following problems: (1) a brief survey on wireless mobile communication technologies including evolution, history evolution (e.g., chain of system generations 0G, 1G, 2G, 3G, 4G, 5G, 6G, 7G); (2) using a hierarchical structural modular approach to the generations of the wireless communication systems (i.e., hierarchical combinatorial modeling of the communication technologies), (3) illustrative usage of two-stage combinatorial approach to improvement/forecasting of the communication technology (a version of 5G) (on the basis of multiple choice problem). Numerical examples illustrate the suggested combinatorial approach.
Learning a 3D descriptor for cross-source point cloud registration from synthetic data
Venue: arXiv cs.CV 2017
Year: 2017

As the development of 3D sensors, registration of 3D data (e.g. point cloud) coming from different kind of sensor is dispensable and shows great demanding. However, point cloud registration between different sensors is challenging because of the variant of density, missing data, different viewpoint, noise and outliers, and geometric transformation. In this paper, we propose a method to learn a 3D descriptor for finding the correspondent relations between these challenging point clouds. To train the deep learning framework, we use synthetic 3D point cloud as input. Starting from synthetic dataset, we use region-based sampling method to select reasonable, large and diverse training samples from synthetic samples. Then, we use data augmentation to extend our network be robust to rotation transformation. We focus our work on more general cases that point clouds coming from different sensors, named cross-source point cloud. The experiments show that our descriptor is not only able to generalize to new scenes, but also generalize to different sensors. The results demonstrate that the proposed method successfully aligns two 3D cross-source point clouds which outperforms state-of-the-art method.
NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI
Venue: arXiv cs.CV 2017
Year: 2017

Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging technique which is able to detect the principal directions of water diffusion as well as neurites density in the human brain. Exploiting the ability of Spherical Harmonics (SH) to model spherical functions, we propose a new reconstruction model for DMRI data which is able to estimate both the fiber Orientation Distribution Function (fODF) and the relative volume fractions of the neurites in each voxel, which is robust to multiple fiber crossings. We consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired single fiber diffusion signal to be derived from three compartments: intracellular, extracellular, and cerebrospinal fluid. The model, called NODDI-SH, is derived by convolving the single fiber response with the fODF in each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density in a unified mathematical model providing efficient, robust and accurate results. Results were validated on simulated data and tested on \textit{in-vivo} data of human brain, and compared to and Constrained Spherical Deconvolution (CSD) for benchmarking. Results revealed competitive performance in all respects and inherent adaptivity to local microstructure, while sensibly reducing the computational cost. We also investigated NODDI-SH performance when only a limited number of samples are available for the fitting, demonstrating that 60 samples are enough to obtain reliable results. The fast computational time and the low number of signal samples required, make NODDI-SH feasible for clinical application.
A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling
Venue: arXiv cs.CV 2017
Year: 2017

While diffusion MRI has been extremely promising in the study of MTBI, identifying patients with recent MTBI remains a challenge. The literature is mixed with regard to localizing injury in these patients, however, gray matter such as the thalamus and white matter including the corpus callosum and frontal deep white matter have been repeatedly implicated as areas at high risk for injury. The purpose of this study is to develop a machine learning framework to classify MTBI patients and controls using features derived from multi-shell diffusion MRI in the thalamus, frontal white matter and corpus callosum.